The present disclosure relates to an imaging device using compressed sensing.
In recent years, an image processing technique using “compressed sensing” has drawn attention. This technique is a technique in which a plurality of pixel values are added up and thus an image is captured, thereby compressing the image, and the image is reconstructed by image processing. Normally, when additional imaging is performed, an information amount of an image is lost, the image quality of a reconstruction image is greatly degraded. However, in compressed sensing, image reconstruction using the sparsity of the image is performed, so that a reconstruction image with image quality not inferior to that of an uncompressed image may be obtained while the amount of data is reduced in additional imaging (see, for example, Y. Oike and A. E. Gamal, “A 256×256 CMOS Image Sensor with ΔΣ-Based Single-Shot Compressed Sensing,” IEEE International Solid-State Circuits Conference (ISSCC) Dig. of Tech. Papers, pp. 386-387, 2012).
The expression “an image is sparse” herein means a phenomenon in which, when an image is projected by a wavelet transform, a discrete cosine transform (DCT), or the like, many coefficient values are substantially zero. As an image reconstruction method using the sparsity of an image, L0 norm minimization or L1 norm minimization is used in compressed sensing.
In compressed sensing, a data amount may be compressed by simple additional processing before an analog digital converter (which will abbreviated as “ADC”, as appropriate) in an image element, and therefore, a drive frequency of ADC may be reduced. Thus, low power consumption, a high SN ratio, and reduction in communication band may be realized.
Japanese Unexamined Patent Publication No. 2010-245955 describes a solid-state image sensor using the concept of compressed sensing. In the solid-state image sensor, a different wiring is coupled to each of a plurality of pixels, and a plurality of pixels of a pixel group are sequentially driven with timings with their phases being shifted and thus reads out a signal. With this configuration, a sample and hold circuit is not needed, and degradation of image quality due to noise increase, increase in an area, and reduction in speed may be reduced.
A method in which compressed sensing is applied to an image using Improved Iterative Curvelet Thresholding method is described in J. Ma, “Improved Iterative Curvelet Thresholding for Compressed Sensing and Measurement,” IEEE Transactions on Instrumentation and Measurement, Vol. 60, Iss. 1, pp. 126-136, 2011.
The following are related art documents: Japanese Unexamined Patent Publication No. 2010-245955; Japanese Unexamined Patent Publication No. 2004-32517; Toshiyuki Tanaka, “Mathematics of Compressed Sensing,” IEICE Fundamentals Review, vol. 4, no. 1, pp. 39-47, 2010; D. Takhar, J. N. Laska, M. B. Wakin. M. F. Durate, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A New Compressive Imaging Camera Architecture using optical-domain compression,” Proc. of Computational Imaging IV at SPIE Electronic Imaging, 2006; Y. Oike and A. E. Gamal, “A 256×256 CMOS Image Sensor with ΔΣ-Based Single-Shot Compressed Sensing,” IEEE International Solid-State Circuits Conference (ISSCC) Dig. of Tech. Papers, pp. 386-387, 2012; J. Ma, “Improved Iterative Curvelet Thresholding for Compressed Sensing and Measurement,” IEEE Transactions on Instrumentation and Measurement, Vol. 60, Iss. 1, pp. 126-136, 2011; Toshihide Ibaraki, Masao Fukushima, “Method of Optimization,” Information mathematics course, vol. 14, Kyoritsu Shuppan Co., Ltd., pp. 159-164, Jul. 20, 1993, First Edition/First Copy; and Makoto Nakashizuka, “Sparse Signal Representation and Its Image Processing Application,” Journal of the Institute of Image Information and Television Engineers, Vol. 65, No. 10, pp. 1381-1386.
However, the sparsity of an image on which compressed sending is premised is not necessarily achieved in a picture. For example, in an image with a high degree of randomness in which small objects scatter, the sparsity is poor. Therefore, in such an image, even when the method described in Toshiyuki Tanaka, “Mathematics of Compressed Sensing,” IEICE Fundamentals Review, vol. 4, no. 1, pp. 39-47, 2010, is used, a problem arises in which the image quality of a reconstruction image is degraded.
In order to solve the above-described problem, a technique disclosed herein has been devised, and it is therefore an object to increase the image quality of a reconstruction image in an imaging device using a compressed sensing.